Avery J. C. Noonan

This study evaluates how respiratory tract sampling methods affect inferred microbiome structure and identifies bronchial brushing as yielding a distinct but low-biomass microbial signal.

The study identifies surface glycan layers as primary or optional recognition substrates for phage infection in E. coli O157:H7, revealing their role in receptor access and infection outcomes.

This paper constructs a phage-bacteria interaction atlas for Klebsiella pneumoniae and develops a genome-guided machine learning model to predict phage-host interactions, outperforming traditional methods.

ASPIRE processes and analyzes linked ASV-VOC data from respiratory microbiome studies to enable scalable comparative analysis and standardized file outputs.

The paper develops a genome-based framework to predict bacteriophage infectivity against diverse Pseudomonas aeruginosa strains using machine learning and experimental data.

The paper predicts phage receptor specificity from genome data using large-scale genetic screens and machine learning, achieving high accuracy for E. coli phages.

This paper develops a machine learning framework to predict strain-level phage-host interactions from genomes, achieving high accuracy without phylogenetic constraints.

The paper evaluates CRISPR/Cas9 knockout screens in human cell lines and designs an optimized gRNA library to improve gene knockout efficiency and accuracy.